A Biologically Inspired Connectionist Architecture for Directing Attention to Salient Visual Field Objects

In an attempt to incorporate basic visual attention abilities into existing artificial vision systems, a neural model of the bidirectional interactions within and between the brain regions believed to be involved in human visual attention has been developed. This model currently gives an artificial vision system the ability to attend to salient, or pop-out features and objects within the vision system\u27\u27s field of view. After a review of the physiology of human visual attention, a network model of the aforementioned neural interactions is presented, followed by a demonstration of its performance

Instance Selection using Genetic Algorithms for an Intelligent Ensemble Trading System

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a new approach to instance selection that uses genetic algorithms (GAs) to define a set of target labels that can identify the buying and selling signals and then select instances according to three performance measures of the trading system (i.e., the winning ratio, the payoff ratio, and the profit factor). An intelligent ensemble trading system with instance selection using GAs is then developed for investors in the stock market. An empirical study of the proposed model is conducted using 35 companies from the Dow Jones Industrial Average, the New York Stock Exchange, and the Nasdaq Stock Market from January, 2006 to December, 2016

Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016

Predicting the Daily Return Direction of the Stock Market using Hybrid Machine Learning Algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks

Hedge Fund Replication using Strategy Specific Factors

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication products might offer lower risk-reward performance compared to hedge funds. This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy, and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors. Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones

Stock Market Prediction with Multiple Regression, Fuzzy Type-2 Clustering and Neural Networks

AbstractStock market forecasting research offers many challenges and opportunities, with the forecasting of individual stocks or indexes focusing on forecasting either the level (value) of future market prices, or the direction of market price movement. A three-stage stock market prediction system is introduced in this article. In the first phase, Multiple Regression Analysis is applied to define the economic and financial variables which have a strong relationship with the output. In the second phase, Differential Evolution-based type-2 Fuzzy Clustering is implemented to create a prediction model. For the third phase, a Fuzzy type-2 Neural Network is used to perform the reasoning for future stock price prediction. The results of the network simulation show that the suggested model outperforms traditional models for forecasting stock market prices

Optimizing MACD Parameters via Genetic Algorithms for Soybean Futures

To create profits, traders must time the market correctly and enter and exit positions at ideal times. Finding the optimal time to enter the market can be quite daunting. The soybean market can be volatile and complex. Weather, sentiment, supply, and demand can all affect the price of soybeans. Traders typically use either fundamental analysis or technical analysis to predict the market for soybean futures\u27 contracts. Every agricultural future\u27s contract or security contract is different in its nature, volatility, and structure. Therefore, the purpose of this research is to optimize the moving average convergence divergence parameter values from traditionally used integers, to values that optimize the profit of the soybean market

Noise Canceling in Volatility Forecasting using an Adaptive Neural Network Filter

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold filter is designed to respond to changes in its environment when a GARCH(1,1) model makes errors in its volatility forecast. It is shown that this filter can forecast the noise (errors) in the GARCH(1,1) outputs when there is a non-stationary time series of errors. The model reduces the mean squared errors by 42.9%. A sample portfolio of five stocks from the S&P 500 index from 4/2007 to 12/2010 is studied to illustrate the performance of the model

Estimation of Earthquake Loss Due to Bridge Damage in the St. Louis Metropolitan Area. II: Indirect Losses

An approach to estimate the indirect economic loss due to damaged bridges within the highway system from an earthquake event is presented. The indirect cost considered refers to the increased highway transportation cost only. The study zone covers the St. Louis metropolitan area and its surrounding suburban regions. An earthquake scenario centered in St. Louis, with a magnitude 7.0 is used. The direct earthquake loss was primarily damage to bridges, which causes an increase in travel time and distance within the transportation network. This information is then used as input for the indirect loss model. The indirect loss is examined from an economic perspective. The results reveal that the indirect loss is significant when compared to the direct loss resulting from bridge damage. From the study results, a transportation network planner can prepare an appropriate preventive action plan (such as choosing alternative routes for potential damaged links, as well as reinforcing possible high damage bridges) to reduce the potential losses before the earthquake occurs

Cost Allocation for Transmission Investment using Agent-Based Game Theory

Due to electrical power restructuring, a dramatic change has been made to the generation and transmission sectors of the power industry. Rules and legislation are continuously changing. To promote more competition, transmission has to be expanded or upgraded to remove congestion and market power. The cost allocation of new investment in transmission has to be recalculated. The socialization methods of the past have been shown to be unfair to some market and network participants. The decentralization of cost allocation must be considered. The proposed paper provides a comparison between traditional cost allocation methods and a new cost allocation method based on agent-based game theory. A multigenerator/bus system will be used to compare the cost allocation methods